Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Saturday 11 July 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 171,895 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 171,885 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 14
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 1
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 1
## 132 2020-03-01 London 0
## 133 2020-03-02 London 0
## 134 2020-03-03 London 0
## 135 2020-03-04 London 0
## 136 2020-03-05 London 0
## 137 2020-03-06 London 1
## 138 2020-03-07 London 0
## 139 2020-03-08 London 0
## 140 2020-03-09 London 1
## 141 2020-03-10 London 0
## 142 2020-03-11 London 5
## 143 2020-03-12 London 6
## 144 2020-03-13 London 10
## 145 2020-03-14 London 13
## 146 2020-03-15 London 9
## 147 2020-03-16 London 15
## 148 2020-03-17 London 23
## 149 2020-03-18 London 27
## 150 2020-03-19 London 25
## 151 2020-03-20 London 44
## 152 2020-03-21 London 49
## 153 2020-03-22 London 54
## 154 2020-03-23 London 63
## 155 2020-03-24 London 86
## 156 2020-03-25 London 112
## 157 2020-03-26 London 129
## 158 2020-03-27 London 129
## 159 2020-03-28 London 122
## 160 2020-03-29 London 145
## 161 2020-03-30 London 149
## 162 2020-03-31 London 181
## 163 2020-04-01 London 202
## 164 2020-04-02 London 191
## 165 2020-04-03 London 196
## 166 2020-04-04 London 230
## 167 2020-04-05 London 195
## 168 2020-04-06 London 197
## 169 2020-04-07 London 220
## 170 2020-04-08 London 238
## 171 2020-04-09 London 206
## 172 2020-04-10 London 170
## 173 2020-04-11 London 178
## 174 2020-04-12 London 158
## 175 2020-04-13 London 166
## 176 2020-04-14 London 143
## 177 2020-04-15 London 142
## 178 2020-04-16 London 140
## 179 2020-04-17 London 100
## 180 2020-04-18 London 101
## 181 2020-04-19 London 103
## 182 2020-04-20 London 95
## 183 2020-04-21 London 94
## 184 2020-04-22 London 109
## 185 2020-04-23 London 77
## 186 2020-04-24 London 71
## 187 2020-04-25 London 58
## 188 2020-04-26 London 53
## 189 2020-04-27 London 51
## 190 2020-04-28 London 44
## 191 2020-04-29 London 45
## 192 2020-04-30 London 40
## 193 2020-05-01 London 41
## 194 2020-05-02 London 41
## 195 2020-05-03 London 36
## 196 2020-05-04 London 30
## 197 2020-05-05 London 25
## 198 2020-05-06 London 37
## 199 2020-05-07 London 37
## 200 2020-05-08 London 30
## 201 2020-05-09 London 23
## 202 2020-05-10 London 26
## 203 2020-05-11 London 18
## 204 2020-05-12 London 18
## 205 2020-05-13 London 17
## 206 2020-05-14 London 20
## 207 2020-05-15 London 18
## 208 2020-05-16 London 14
## 209 2020-05-17 London 15
## 210 2020-05-18 London 10
## 211 2020-05-19 London 14
## 212 2020-05-20 London 19
## 213 2020-05-21 London 12
## 214 2020-05-22 London 10
## 215 2020-05-23 London 6
## 216 2020-05-24 London 7
## 217 2020-05-25 London 9
## 218 2020-05-26 London 13
## 219 2020-05-27 London 7
## 220 2020-05-28 London 8
## 221 2020-05-29 London 7
## 222 2020-05-30 London 12
## 223 2020-05-31 London 6
## 224 2020-06-01 London 10
## 225 2020-06-02 London 8
## 226 2020-06-03 London 6
## 227 2020-06-04 London 8
## 228 2020-06-05 London 4
## 229 2020-06-06 London 0
## 230 2020-06-07 London 5
## 231 2020-06-08 London 5
## 232 2020-06-09 London 4
## 233 2020-06-10 London 7
## 234 2020-06-11 London 5
## 235 2020-06-12 London 3
## 236 2020-06-13 London 3
## 237 2020-06-14 London 3
## 238 2020-06-15 London 1
## 239 2020-06-16 London 2
## 240 2020-06-17 London 1
## 241 2020-06-18 London 2
## 242 2020-06-19 London 5
## 243 2020-06-20 London 3
## 244 2020-06-21 London 4
## 245 2020-06-22 London 2
## 246 2020-06-23 London 1
## 247 2020-06-24 London 4
## 248 2020-06-25 London 3
## 249 2020-06-26 London 2
## 250 2020-06-27 London 1
## 251 2020-06-28 London 2
## 252 2020-06-29 London 2
## 253 2020-06-30 London 1
## 254 2020-07-01 London 2
## 255 2020-07-02 London 2
## 256 2020-07-03 London 2
## 257 2020-07-04 London 1
## 258 2020-07-05 London 3
## 259 2020-07-06 London 2
## 260 2020-07-07 London 1
## 261 2020-07-08 London 1
## 262 2020-07-09 London 0
## 263 2020-03-01 Midlands 0
## 264 2020-03-02 Midlands 0
## 265 2020-03-03 Midlands 1
## 266 2020-03-04 Midlands 0
## 267 2020-03-05 Midlands 0
## 268 2020-03-06 Midlands 0
## 269 2020-03-07 Midlands 0
## 270 2020-03-08 Midlands 2
## 271 2020-03-09 Midlands 1
## 272 2020-03-10 Midlands 0
## 273 2020-03-11 Midlands 2
## 274 2020-03-12 Midlands 6
## 275 2020-03-13 Midlands 5
## 276 2020-03-14 Midlands 4
## 277 2020-03-15 Midlands 5
## 278 2020-03-16 Midlands 11
## 279 2020-03-17 Midlands 8
## 280 2020-03-18 Midlands 13
## 281 2020-03-19 Midlands 8
## 282 2020-03-20 Midlands 28
## 283 2020-03-21 Midlands 13
## 284 2020-03-22 Midlands 31
## 285 2020-03-23 Midlands 33
## 286 2020-03-24 Midlands 41
## 287 2020-03-25 Midlands 48
## 288 2020-03-26 Midlands 64
## 289 2020-03-27 Midlands 72
## 290 2020-03-28 Midlands 89
## 291 2020-03-29 Midlands 92
## 292 2020-03-30 Midlands 90
## 293 2020-03-31 Midlands 123
## 294 2020-04-01 Midlands 140
## 295 2020-04-02 Midlands 142
## 296 2020-04-03 Midlands 124
## 297 2020-04-04 Midlands 151
## 298 2020-04-05 Midlands 164
## 299 2020-04-06 Midlands 140
## 300 2020-04-07 Midlands 123
## 301 2020-04-08 Midlands 186
## 302 2020-04-09 Midlands 139
## 303 2020-04-10 Midlands 127
## 304 2020-04-11 Midlands 142
## 305 2020-04-12 Midlands 139
## 306 2020-04-13 Midlands 120
## 307 2020-04-14 Midlands 116
## 308 2020-04-15 Midlands 147
## 309 2020-04-16 Midlands 102
## 310 2020-04-17 Midlands 118
## 311 2020-04-18 Midlands 115
## 312 2020-04-19 Midlands 92
## 313 2020-04-20 Midlands 107
## 314 2020-04-21 Midlands 86
## 315 2020-04-22 Midlands 78
## 316 2020-04-23 Midlands 103
## 317 2020-04-24 Midlands 79
## 318 2020-04-25 Midlands 72
## 319 2020-04-26 Midlands 81
## 320 2020-04-27 Midlands 74
## 321 2020-04-28 Midlands 68
## 322 2020-04-29 Midlands 53
## 323 2020-04-30 Midlands 56
## 324 2020-05-01 Midlands 64
## 325 2020-05-02 Midlands 51
## 326 2020-05-03 Midlands 52
## 327 2020-05-04 Midlands 61
## 328 2020-05-05 Midlands 59
## 329 2020-05-06 Midlands 59
## 330 2020-05-07 Midlands 48
## 331 2020-05-08 Midlands 34
## 332 2020-05-09 Midlands 37
## 333 2020-05-10 Midlands 42
## 334 2020-05-11 Midlands 33
## 335 2020-05-12 Midlands 45
## 336 2020-05-13 Midlands 40
## 337 2020-05-14 Midlands 37
## 338 2020-05-15 Midlands 40
## 339 2020-05-16 Midlands 34
## 340 2020-05-17 Midlands 31
## 341 2020-05-18 Midlands 35
## 342 2020-05-19 Midlands 34
## 343 2020-05-20 Midlands 36
## 344 2020-05-21 Midlands 32
## 345 2020-05-22 Midlands 27
## 346 2020-05-23 Midlands 34
## 347 2020-05-24 Midlands 20
## 348 2020-05-25 Midlands 26
## 349 2020-05-26 Midlands 33
## 350 2020-05-27 Midlands 29
## 351 2020-05-28 Midlands 28
## 352 2020-05-29 Midlands 20
## 353 2020-05-30 Midlands 21
## 354 2020-05-31 Midlands 22
## 355 2020-06-01 Midlands 20
## 356 2020-06-02 Midlands 22
## 357 2020-06-03 Midlands 24
## 358 2020-06-04 Midlands 16
## 359 2020-06-05 Midlands 21
## 360 2020-06-06 Midlands 20
## 361 2020-06-07 Midlands 17
## 362 2020-06-08 Midlands 16
## 363 2020-06-09 Midlands 18
## 364 2020-06-10 Midlands 15
## 365 2020-06-11 Midlands 13
## 366 2020-06-12 Midlands 12
## 367 2020-06-13 Midlands 6
## 368 2020-06-14 Midlands 18
## 369 2020-06-15 Midlands 12
## 370 2020-06-16 Midlands 15
## 371 2020-06-17 Midlands 10
## 372 2020-06-18 Midlands 15
## 373 2020-06-19 Midlands 10
## 374 2020-06-20 Midlands 15
## 375 2020-06-21 Midlands 13
## 376 2020-06-22 Midlands 13
## 377 2020-06-23 Midlands 16
## 378 2020-06-24 Midlands 15
## 379 2020-06-25 Midlands 18
## 380 2020-06-26 Midlands 5
## 381 2020-06-27 Midlands 5
## 382 2020-06-28 Midlands 6
## 383 2020-06-29 Midlands 6
## 384 2020-06-30 Midlands 6
## 385 2020-07-01 Midlands 7
## 386 2020-07-02 Midlands 9
## 387 2020-07-03 Midlands 3
## 388 2020-07-04 Midlands 4
## 389 2020-07-05 Midlands 6
## 390 2020-07-06 Midlands 5
## 391 2020-07-07 Midlands 1
## 392 2020-07-08 Midlands 3
## 393 2020-07-09 Midlands 0
## 394 2020-03-01 North East and Yorkshire 0
## 395 2020-03-02 North East and Yorkshire 0
## 396 2020-03-03 North East and Yorkshire 0
## 397 2020-03-04 North East and Yorkshire 0
## 398 2020-03-05 North East and Yorkshire 0
## 399 2020-03-06 North East and Yorkshire 0
## 400 2020-03-07 North East and Yorkshire 0
## 401 2020-03-08 North East and Yorkshire 0
## 402 2020-03-09 North East and Yorkshire 0
## 403 2020-03-10 North East and Yorkshire 0
## 404 2020-03-11 North East and Yorkshire 0
## 405 2020-03-12 North East and Yorkshire 0
## 406 2020-03-13 North East and Yorkshire 0
## 407 2020-03-14 North East and Yorkshire 0
## 408 2020-03-15 North East and Yorkshire 2
## 409 2020-03-16 North East and Yorkshire 3
## 410 2020-03-17 North East and Yorkshire 1
## 411 2020-03-18 North East and Yorkshire 2
## 412 2020-03-19 North East and Yorkshire 6
## 413 2020-03-20 North East and Yorkshire 5
## 414 2020-03-21 North East and Yorkshire 6
## 415 2020-03-22 North East and Yorkshire 7
## 416 2020-03-23 North East and Yorkshire 9
## 417 2020-03-24 North East and Yorkshire 8
## 418 2020-03-25 North East and Yorkshire 18
## 419 2020-03-26 North East and Yorkshire 21
## 420 2020-03-27 North East and Yorkshire 28
## 421 2020-03-28 North East and Yorkshire 35
## 422 2020-03-29 North East and Yorkshire 38
## 423 2020-03-30 North East and Yorkshire 64
## 424 2020-03-31 North East and Yorkshire 60
## 425 2020-04-01 North East and Yorkshire 67
## 426 2020-04-02 North East and Yorkshire 75
## 427 2020-04-03 North East and Yorkshire 100
## 428 2020-04-04 North East and Yorkshire 105
## 429 2020-04-05 North East and Yorkshire 92
## 430 2020-04-06 North East and Yorkshire 96
## 431 2020-04-07 North East and Yorkshire 102
## 432 2020-04-08 North East and Yorkshire 107
## 433 2020-04-09 North East and Yorkshire 111
## 434 2020-04-10 North East and Yorkshire 117
## 435 2020-04-11 North East and Yorkshire 98
## 436 2020-04-12 North East and Yorkshire 84
## 437 2020-04-13 North East and Yorkshire 94
## 438 2020-04-14 North East and Yorkshire 107
## 439 2020-04-15 North East and Yorkshire 96
## 440 2020-04-16 North East and Yorkshire 103
## 441 2020-04-17 North East and Yorkshire 88
## 442 2020-04-18 North East and Yorkshire 95
## 443 2020-04-19 North East and Yorkshire 88
## 444 2020-04-20 North East and Yorkshire 100
## 445 2020-04-21 North East and Yorkshire 76
## 446 2020-04-22 North East and Yorkshire 84
## 447 2020-04-23 North East and Yorkshire 63
## 448 2020-04-24 North East and Yorkshire 72
## 449 2020-04-25 North East and Yorkshire 69
## 450 2020-04-26 North East and Yorkshire 65
## 451 2020-04-27 North East and Yorkshire 65
## 452 2020-04-28 North East and Yorkshire 57
## 453 2020-04-29 North East and Yorkshire 69
## 454 2020-04-30 North East and Yorkshire 57
## 455 2020-05-01 North East and Yorkshire 64
## 456 2020-05-02 North East and Yorkshire 48
## 457 2020-05-03 North East and Yorkshire 40
## 458 2020-05-04 North East and Yorkshire 49
## 459 2020-05-05 North East and Yorkshire 40
## 460 2020-05-06 North East and Yorkshire 51
## 461 2020-05-07 North East and Yorkshire 45
## 462 2020-05-08 North East and Yorkshire 42
## 463 2020-05-09 North East and Yorkshire 44
## 464 2020-05-10 North East and Yorkshire 40
## 465 2020-05-11 North East and Yorkshire 29
## 466 2020-05-12 North East and Yorkshire 27
## 467 2020-05-13 North East and Yorkshire 28
## 468 2020-05-14 North East and Yorkshire 31
## 469 2020-05-15 North East and Yorkshire 32
## 470 2020-05-16 North East and Yorkshire 35
## 471 2020-05-17 North East and Yorkshire 26
## 472 2020-05-18 North East and Yorkshire 30
## 473 2020-05-19 North East and Yorkshire 27
## 474 2020-05-20 North East and Yorkshire 22
## 475 2020-05-21 North East and Yorkshire 33
## 476 2020-05-22 North East and Yorkshire 22
## 477 2020-05-23 North East and Yorkshire 18
## 478 2020-05-24 North East and Yorkshire 26
## 479 2020-05-25 North East and Yorkshire 21
## 480 2020-05-26 North East and Yorkshire 21
## 481 2020-05-27 North East and Yorkshire 22
## 482 2020-05-28 North East and Yorkshire 21
## 483 2020-05-29 North East and Yorkshire 25
## 484 2020-05-30 North East and Yorkshire 20
## 485 2020-05-31 North East and Yorkshire 20
## 486 2020-06-01 North East and Yorkshire 17
## 487 2020-06-02 North East and Yorkshire 23
## 488 2020-06-03 North East and Yorkshire 23
## 489 2020-06-04 North East and Yorkshire 17
## 490 2020-06-05 North East and Yorkshire 18
## 491 2020-06-06 North East and Yorkshire 21
## 492 2020-06-07 North East and Yorkshire 14
## 493 2020-06-08 North East and Yorkshire 11
## 494 2020-06-09 North East and Yorkshire 12
## 495 2020-06-10 North East and Yorkshire 19
## 496 2020-06-11 North East and Yorkshire 7
## 497 2020-06-12 North East and Yorkshire 9
## 498 2020-06-13 North East and Yorkshire 10
## 499 2020-06-14 North East and Yorkshire 11
## 500 2020-06-15 North East and Yorkshire 9
## 501 2020-06-16 North East and Yorkshire 10
## 502 2020-06-17 North East and Yorkshire 9
## 503 2020-06-18 North East and Yorkshire 11
## 504 2020-06-19 North East and Yorkshire 6
## 505 2020-06-20 North East and Yorkshire 4
## 506 2020-06-21 North East and Yorkshire 4
## 507 2020-06-22 North East and Yorkshire 6
## 508 2020-06-23 North East and Yorkshire 7
## 509 2020-06-24 North East and Yorkshire 10
## 510 2020-06-25 North East and Yorkshire 4
## 511 2020-06-26 North East and Yorkshire 7
## 512 2020-06-27 North East and Yorkshire 3
## 513 2020-06-28 North East and Yorkshire 5
## 514 2020-06-29 North East and Yorkshire 2
## 515 2020-06-30 North East and Yorkshire 4
## 516 2020-07-01 North East and Yorkshire 1
## 517 2020-07-02 North East and Yorkshire 4
## 518 2020-07-03 North East and Yorkshire 3
## 519 2020-07-04 North East and Yorkshire 4
## 520 2020-07-05 North East and Yorkshire 2
## 521 2020-07-06 North East and Yorkshire 2
## 522 2020-07-07 North East and Yorkshire 3
## 523 2020-07-08 North East and Yorkshire 2
## 524 2020-07-09 North East and Yorkshire 0
## 525 2020-03-01 North West 0
## 526 2020-03-02 North West 0
## 527 2020-03-03 North West 0
## 528 2020-03-04 North West 0
## 529 2020-03-05 North West 1
## 530 2020-03-06 North West 0
## 531 2020-03-07 North West 0
## 532 2020-03-08 North West 1
## 533 2020-03-09 North West 0
## 534 2020-03-10 North West 0
## 535 2020-03-11 North West 0
## 536 2020-03-12 North West 2
## 537 2020-03-13 North West 3
## 538 2020-03-14 North West 1
## 539 2020-03-15 North West 4
## 540 2020-03-16 North West 2
## 541 2020-03-17 North West 4
## 542 2020-03-18 North West 6
## 543 2020-03-19 North West 7
## 544 2020-03-20 North West 10
## 545 2020-03-21 North West 11
## 546 2020-03-22 North West 13
## 547 2020-03-23 North West 15
## 548 2020-03-24 North West 21
## 549 2020-03-25 North West 21
## 550 2020-03-26 North West 29
## 551 2020-03-27 North West 36
## 552 2020-03-28 North West 28
## 553 2020-03-29 North West 46
## 554 2020-03-30 North West 67
## 555 2020-03-31 North West 52
## 556 2020-04-01 North West 86
## 557 2020-04-02 North West 96
## 558 2020-04-03 North West 95
## 559 2020-04-04 North West 98
## 560 2020-04-05 North West 102
## 561 2020-04-06 North West 100
## 562 2020-04-07 North West 135
## 563 2020-04-08 North West 127
## 564 2020-04-09 North West 119
## 565 2020-04-10 North West 117
## 566 2020-04-11 North West 138
## 567 2020-04-12 North West 125
## 568 2020-04-13 North West 129
## 569 2020-04-14 North West 131
## 570 2020-04-15 North West 114
## 571 2020-04-16 North West 135
## 572 2020-04-17 North West 98
## 573 2020-04-18 North West 113
## 574 2020-04-19 North West 71
## 575 2020-04-20 North West 83
## 576 2020-04-21 North West 76
## 577 2020-04-22 North West 86
## 578 2020-04-23 North West 85
## 579 2020-04-24 North West 66
## 580 2020-04-25 North West 66
## 581 2020-04-26 North West 55
## 582 2020-04-27 North West 54
## 583 2020-04-28 North West 57
## 584 2020-04-29 North West 63
## 585 2020-04-30 North West 59
## 586 2020-05-01 North West 45
## 587 2020-05-02 North West 56
## 588 2020-05-03 North West 55
## 589 2020-05-04 North West 48
## 590 2020-05-05 North West 48
## 591 2020-05-06 North West 44
## 592 2020-05-07 North West 49
## 593 2020-05-08 North West 42
## 594 2020-05-09 North West 30
## 595 2020-05-10 North West 42
## 596 2020-05-11 North West 35
## 597 2020-05-12 North West 38
## 598 2020-05-13 North West 25
## 599 2020-05-14 North West 26
## 600 2020-05-15 North West 33
## 601 2020-05-16 North West 32
## 602 2020-05-17 North West 24
## 603 2020-05-18 North West 31
## 604 2020-05-19 North West 35
## 605 2020-05-20 North West 27
## 606 2020-05-21 North West 27
## 607 2020-05-22 North West 26
## 608 2020-05-23 North West 31
## 609 2020-05-24 North West 26
## 610 2020-05-25 North West 31
## 611 2020-05-26 North West 27
## 612 2020-05-27 North West 27
## 613 2020-05-28 North West 28
## 614 2020-05-29 North West 20
## 615 2020-05-30 North West 19
## 616 2020-05-31 North West 13
## 617 2020-06-01 North West 12
## 618 2020-06-02 North West 27
## 619 2020-06-03 North West 22
## 620 2020-06-04 North West 22
## 621 2020-06-05 North West 16
## 622 2020-06-06 North West 26
## 623 2020-06-07 North West 20
## 624 2020-06-08 North West 23
## 625 2020-06-09 North West 17
## 626 2020-06-10 North West 16
## 627 2020-06-11 North West 16
## 628 2020-06-12 North West 11
## 629 2020-06-13 North West 10
## 630 2020-06-14 North West 15
## 631 2020-06-15 North West 16
## 632 2020-06-16 North West 15
## 633 2020-06-17 North West 12
## 634 2020-06-18 North West 13
## 635 2020-06-19 North West 7
## 636 2020-06-20 North West 11
## 637 2020-06-21 North West 7
## 638 2020-06-22 North West 11
## 639 2020-06-23 North West 13
## 640 2020-06-24 North West 13
## 641 2020-06-25 North West 15
## 642 2020-06-26 North West 5
## 643 2020-06-27 North West 7
## 644 2020-06-28 North West 9
## 645 2020-06-29 North West 6
## 646 2020-06-30 North West 6
## 647 2020-07-01 North West 2
## 648 2020-07-02 North West 6
## 649 2020-07-03 North West 5
## 650 2020-07-04 North West 3
## 651 2020-07-05 North West 3
## 652 2020-07-06 North West 9
## 653 2020-07-07 North West 5
## 654 2020-07-08 North West 2
## 655 2020-07-09 North West 5
## 656 2020-03-01 South East 0
## 657 2020-03-02 South East 0
## 658 2020-03-03 South East 1
## 659 2020-03-04 South East 0
## 660 2020-03-05 South East 1
## 661 2020-03-06 South East 0
## 662 2020-03-07 South East 0
## 663 2020-03-08 South East 1
## 664 2020-03-09 South East 1
## 665 2020-03-10 South East 1
## 666 2020-03-11 South East 1
## 667 2020-03-12 South East 0
## 668 2020-03-13 South East 1
## 669 2020-03-14 South East 1
## 670 2020-03-15 South East 5
## 671 2020-03-16 South East 8
## 672 2020-03-17 South East 7
## 673 2020-03-18 South East 10
## 674 2020-03-19 South East 9
## 675 2020-03-20 South East 13
## 676 2020-03-21 South East 7
## 677 2020-03-22 South East 25
## 678 2020-03-23 South East 20
## 679 2020-03-24 South East 22
## 680 2020-03-25 South East 29
## 681 2020-03-26 South East 35
## 682 2020-03-27 South East 34
## 683 2020-03-28 South East 36
## 684 2020-03-29 South East 55
## 685 2020-03-30 South East 58
## 686 2020-03-31 South East 65
## 687 2020-04-01 South East 66
## 688 2020-04-02 South East 55
## 689 2020-04-03 South East 72
## 690 2020-04-04 South East 80
## 691 2020-04-05 South East 82
## 692 2020-04-06 South East 88
## 693 2020-04-07 South East 100
## 694 2020-04-08 South East 83
## 695 2020-04-09 South East 104
## 696 2020-04-10 South East 88
## 697 2020-04-11 South East 88
## 698 2020-04-12 South East 88
## 699 2020-04-13 South East 84
## 700 2020-04-14 South East 65
## 701 2020-04-15 South East 72
## 702 2020-04-16 South East 56
## 703 2020-04-17 South East 86
## 704 2020-04-18 South East 57
## 705 2020-04-19 South East 70
## 706 2020-04-20 South East 87
## 707 2020-04-21 South East 51
## 708 2020-04-22 South East 54
## 709 2020-04-23 South East 57
## 710 2020-04-24 South East 64
## 711 2020-04-25 South East 51
## 712 2020-04-26 South East 51
## 713 2020-04-27 South East 40
## 714 2020-04-28 South East 40
## 715 2020-04-29 South East 47
## 716 2020-04-30 South East 29
## 717 2020-05-01 South East 37
## 718 2020-05-02 South East 36
## 719 2020-05-03 South East 17
## 720 2020-05-04 South East 35
## 721 2020-05-05 South East 29
## 722 2020-05-06 South East 25
## 723 2020-05-07 South East 27
## 724 2020-05-08 South East 26
## 725 2020-05-09 South East 28
## 726 2020-05-10 South East 19
## 727 2020-05-11 South East 25
## 728 2020-05-12 South East 27
## 729 2020-05-13 South East 18
## 730 2020-05-14 South East 32
## 731 2020-05-15 South East 25
## 732 2020-05-16 South East 22
## 733 2020-05-17 South East 18
## 734 2020-05-18 South East 22
## 735 2020-05-19 South East 12
## 736 2020-05-20 South East 22
## 737 2020-05-21 South East 15
## 738 2020-05-22 South East 17
## 739 2020-05-23 South East 21
## 740 2020-05-24 South East 17
## 741 2020-05-25 South East 13
## 742 2020-05-26 South East 19
## 743 2020-05-27 South East 18
## 744 2020-05-28 South East 12
## 745 2020-05-29 South East 21
## 746 2020-05-30 South East 8
## 747 2020-05-31 South East 12
## 748 2020-06-01 South East 11
## 749 2020-06-02 South East 13
## 750 2020-06-03 South East 18
## 751 2020-06-04 South East 11
## 752 2020-06-05 South East 11
## 753 2020-06-06 South East 10
## 754 2020-06-07 South East 12
## 755 2020-06-08 South East 8
## 756 2020-06-09 South East 10
## 757 2020-06-10 South East 11
## 758 2020-06-11 South East 5
## 759 2020-06-12 South East 6
## 760 2020-06-13 South East 6
## 761 2020-06-14 South East 7
## 762 2020-06-15 South East 8
## 763 2020-06-16 South East 13
## 764 2020-06-17 South East 9
## 765 2020-06-18 South East 4
## 766 2020-06-19 South East 7
## 767 2020-06-20 South East 5
## 768 2020-06-21 South East 3
## 769 2020-06-22 South East 2
## 770 2020-06-23 South East 8
## 771 2020-06-24 South East 7
## 772 2020-06-25 South East 5
## 773 2020-06-26 South East 8
## 774 2020-06-27 South East 8
## 775 2020-06-28 South East 6
## 776 2020-06-29 South East 5
## 777 2020-06-30 South East 5
## 778 2020-07-01 South East 2
## 779 2020-07-02 South East 7
## 780 2020-07-03 South East 3
## 781 2020-07-04 South East 5
## 782 2020-07-05 South East 3
## 783 2020-07-06 South East 3
## 784 2020-07-07 South East 5
## 785 2020-07-08 South East 2
## 786 2020-07-09 South East 0
## 787 2020-03-01 South West 0
## 788 2020-03-02 South West 0
## 789 2020-03-03 South West 0
## 790 2020-03-04 South West 0
## 791 2020-03-05 South West 0
## 792 2020-03-06 South West 0
## 793 2020-03-07 South West 0
## 794 2020-03-08 South West 0
## 795 2020-03-09 South West 0
## 796 2020-03-10 South West 0
## 797 2020-03-11 South West 1
## 798 2020-03-12 South West 0
## 799 2020-03-13 South West 0
## 800 2020-03-14 South West 1
## 801 2020-03-15 South West 0
## 802 2020-03-16 South West 0
## 803 2020-03-17 South West 2
## 804 2020-03-18 South West 2
## 805 2020-03-19 South West 4
## 806 2020-03-20 South West 3
## 807 2020-03-21 South West 6
## 808 2020-03-22 South West 7
## 809 2020-03-23 South West 8
## 810 2020-03-24 South West 7
## 811 2020-03-25 South West 9
## 812 2020-03-26 South West 11
## 813 2020-03-27 South West 13
## 814 2020-03-28 South West 21
## 815 2020-03-29 South West 18
## 816 2020-03-30 South West 23
## 817 2020-03-31 South West 23
## 818 2020-04-01 South West 21
## 819 2020-04-02 South West 23
## 820 2020-04-03 South West 30
## 821 2020-04-04 South West 42
## 822 2020-04-05 South West 32
## 823 2020-04-06 South West 34
## 824 2020-04-07 South West 39
## 825 2020-04-08 South West 47
## 826 2020-04-09 South West 24
## 827 2020-04-10 South West 46
## 828 2020-04-11 South West 43
## 829 2020-04-12 South West 23
## 830 2020-04-13 South West 27
## 831 2020-04-14 South West 24
## 832 2020-04-15 South West 32
## 833 2020-04-16 South West 29
## 834 2020-04-17 South West 33
## 835 2020-04-18 South West 25
## 836 2020-04-19 South West 31
## 837 2020-04-20 South West 26
## 838 2020-04-21 South West 26
## 839 2020-04-22 South West 23
## 840 2020-04-23 South West 17
## 841 2020-04-24 South West 19
## 842 2020-04-25 South West 15
## 843 2020-04-26 South West 27
## 844 2020-04-27 South West 13
## 845 2020-04-28 South West 17
## 846 2020-04-29 South West 15
## 847 2020-04-30 South West 26
## 848 2020-05-01 South West 6
## 849 2020-05-02 South West 7
## 850 2020-05-03 South West 10
## 851 2020-05-04 South West 17
## 852 2020-05-05 South West 14
## 853 2020-05-06 South West 19
## 854 2020-05-07 South West 16
## 855 2020-05-08 South West 6
## 856 2020-05-09 South West 11
## 857 2020-05-10 South West 5
## 858 2020-05-11 South West 8
## 859 2020-05-12 South West 7
## 860 2020-05-13 South West 7
## 861 2020-05-14 South West 6
## 862 2020-05-15 South West 4
## 863 2020-05-16 South West 4
## 864 2020-05-17 South West 6
## 865 2020-05-18 South West 4
## 866 2020-05-19 South West 6
## 867 2020-05-20 South West 1
## 868 2020-05-21 South West 9
## 869 2020-05-22 South West 6
## 870 2020-05-23 South West 6
## 871 2020-05-24 South West 3
## 872 2020-05-25 South West 8
## 873 2020-05-26 South West 11
## 874 2020-05-27 South West 5
## 875 2020-05-28 South West 10
## 876 2020-05-29 South West 7
## 877 2020-05-30 South West 3
## 878 2020-05-31 South West 2
## 879 2020-06-01 South West 7
## 880 2020-06-02 South West 2
## 881 2020-06-03 South West 7
## 882 2020-06-04 South West 2
## 883 2020-06-05 South West 2
## 884 2020-06-06 South West 1
## 885 2020-06-07 South West 3
## 886 2020-06-08 South West 3
## 887 2020-06-09 South West 0
## 888 2020-06-10 South West 1
## 889 2020-06-11 South West 2
## 890 2020-06-12 South West 2
## 891 2020-06-13 South West 2
## 892 2020-06-14 South West 0
## 893 2020-06-15 South West 1
## 894 2020-06-16 South West 2
## 895 2020-06-17 South West 0
## 896 2020-06-18 South West 0
## 897 2020-06-19 South West 0
## 898 2020-06-20 South West 2
## 899 2020-06-21 South West 0
## 900 2020-06-22 South West 1
## 901 2020-06-23 South West 1
## 902 2020-06-24 South West 1
## 903 2020-06-25 South West 0
## 904 2020-06-26 South West 3
## 905 2020-06-27 South West 0
## 906 2020-06-28 South West 0
## 907 2020-06-29 South West 1
## 908 2020-06-30 South West 0
## 909 2020-07-01 South West 0
## 910 2020-07-02 South West 0
## 911 2020-07-03 South West 0
## 912 2020-07-04 South West 0
## 913 2020-07-05 South West 1
## 914 2020-07-06 South West 0
## 915 2020-07-07 South West 0
## 916 2020-07-08 South West 2
## 917 2020-07-09 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Thursday 09 Jul 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -12.3860 -3.8193 -0.1245 3.3015 7.5365
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.674e+00 6.215e-02 75.20 <2e-16 ***
## note_lag 1.388e-05 6.437e-07 21.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 20.92816)
##
## Null deviance: 10524.0 on 69 degrees of freedom
## Residual deviance: 1506.2 on 68 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 107.110840 1.000014
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 94.656518 120.776701
## note_lag 1.000013 1.000015
Rsq(lag_mod)
## [1] 0.8568841
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.14
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.5.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-148 fs_1.4.2 webshot_0.5.2 httr_1.4.1
## [5] rprojroot_1.3-2 tools_4.0.2 backports_1.1.8 utf8_1.1.4
## [9] R6_2.4.1 mgcv_1.8-31 DBI_1.1.0 colorspace_1.4-1
## [13] withr_2.2.0 gridExtra_2.3 tidyselect_1.1.0 sodium_1.1
## [17] curl_4.3 compiler_4.0.2 cli_2.0.2 labeling_0.3
## [21] matchmaker_0.1.1 scales_1.1.1 digest_0.6.25 foreign_0.8-80
## [25] rmarkdown_2.3 pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
## [29] htmlwidgets_1.5.1 rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11
## [33] farver_2.0.3 generics_0.0.2 jsonlite_1.7.0 crosstalk_1.1.0.1
## [37] car_3.0-8 zip_2.0.4 magrittr_1.5 kyotil_2019.11-22
## [41] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
## [45] viridis_0.5.1 abind_1.4-5 lifecycle_0.2.0 stringi_1.4.6
## [49] yaml_2.2.1 carData_3.0-4 snakecase_0.11.0 MASS_7.3-51.6
## [53] plyr_1.8.6 grid_4.0.2 blob_1.2.1 crayon_1.3.4
## [57] lattice_0.20-41 cowplot_1.0.0 splines_4.0.2 haven_2.3.1
## [61] hms_0.5.3 knitr_1.29 pillar_1.4.6 boot_1.3-25
## [65] ggsignif_0.6.0 reprex_0.3.0 glue_1.4.1 evaluate_0.14
## [69] data.table_1.12.8 modelr_0.1.8 vctrs_0.3.1 selectr_0.4-2
## [73] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 xfun_0.15
## [77] openxlsx_4.1.5 broom_0.7.0 rstatix_0.6.0 survival_3.1-12
## [81] viridisLite_0.3.0 ellipsis_0.3.1